Predicting salaries

In this exercise, you will use the census income dataset to predict if individuals have a salary of more than $50K/year or not.

Remember that you should specify the bounds as a parameter when creating the private model to ensure no additional privacy loss or information leakage. Usually, you can choose the bounds independently of the data, using domain knowledge or search with a DP histogram.

The dataset has been loaded and split into X_train, y_train, X_test, and y_test. The classifier is available as dp_GaussianNB.

This exercise is part of the course

Data Privacy and Anonymization in Python

Exercise instructions

Set the bounds of the model by calculating the min and max values in the training data and adding random noise by subtracting and adding random numbers in a range from 5 to 40 for the 5 columns in our data.
Create a dp_GaussianNB classifier with an epsilon of 0.5 and the previously created bounds.
Fit the model to the data and see the score.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Set the min and max of bounds for the data and add noise using random
bounds = (X_train.____(axis=0) - random.____(range(5, 40), 5), 
          ____)

# Built the classifier with epsilon of 0.5
dp_clf = ____(epsilon=____, bounds=____)

# Fit the model to the data and print the score
____
print("The accuracy of the differentially private model is ",
       dp_clf.score(X_test, y_test))

Edit and Run Code

This exercise is part of the course

Data Privacy and Anonymization in Python

AdvancedSkill Level

4.9+

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Get ready to apply anonymization techniques such as data suppression, masking, synthetic data generation, and generalization. In this chapter, you’ll learn how to distinguish between sensitive and non-sensitive personally identifiable information (PII), quasi-identifiers, and the basics of the GDPR. You'll also encounter real-life examples of what can go wrong if you don't follow these best practices.

Exercise 1: What's private, and why do we care?Exercise 2: Privacy is power Exercise 3: Is it sensitive or non-sensitive?Exercise 4: Suppression of sensitive attributes Exercise 5: Data masking and data generation with Faker Exercise 6: Masking sensitive PII Exercise 7: Removing names with faker Exercise 8: Anonymizing with data generalization Exercise 9: Reducing identification risk with generalization Exercise 10: Data aggregation and data generalization Exercise 11: Top and bottom coding White House salaries

Discover how to anonymize data by sampling from datasets following the probability distribution of the columns. You’ll then learn how to apply the k-anonymity privacy model to prevent linkage or re-identification attacks and use hierarchies to perform data generalization in categorical variables.

Exercise 1: Anonymizing categorical data Exercise 2: Explore the distribution of data Exercise 3: Sampling from the same probability distribution Exercise 4: Anonymizing continuous data Exercise 5: Different distributions Exercise 6: Sampling from the best continuous distribution Exercise 7: Introduction to K-anonymity Exercise 8: Privacy attributes Exercise 9: Generalizing into ranges Exercise 10: Generalizing data using hierarchies Exercise 11: Using hierarchies for categorical data Exercise 12: K-anonymizing a dataset

Learn about differential privacy, the model used by major technology companies such as Apple, Google, and Uber. In this chapter, you’ll explore data by generating private histograms and computing private averages in data. You’ll also create differentially private machine learning models that allow businesses to increase the utility of their data.

Exercise 1: Introduction to differential privacy Exercise 2: Epsilon (ϵ): the magic number Exercise 3: Histograms with differential privacy Exercise 4: Privacy budgets Exercise 5: Using privacy budgets Exercise 6: When no budget is left Exercise 7: Exploring data with a privacy budget accountant Exercise 8: Differentially private machine learning models Exercise 9: Build a differentially private classifier Exercise 10: Predicting salaries

Current Exercise

Exercise 11: Differentially private clustering models Exercise 12: Pre-processing data Exercise 13: Segmenting customers

In this final chapter, you’ll learn how to apply dimensionality reduction methods such as principal component analysis (PCA) to anonymize large multi-column datasets. You’ll then use Faker to generate realistic and consistent datasets, and scikit-learn to create synthetic datasets that follow a normal distribution. Lastly, you’ll tie everything you learned in this course together as you combine multiple techniques to safely release datasets to the public.

Exercise 1: PCA for anonymization Exercise 2: Anonymization of high-dimensional data Exercise 3: Data masking with PCA Exercise 4: Generating realistic datasets with Faker Exercise 5: Consistent synthetic dataset Exercise 6: Datasets with the same probabilistic distribution Exercise 7: Creating synthetic datasets using scikit-learn Exercise 8: Generating datasets for classification Exercise 9: Generating datasets for clustering Exercise 10: Safely release datasets to the public Exercise 11: Exploring and pseudonymizing a dataset Exercise 12: Preparing employee data for safe release Exercise 13: Great work!